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TLQP: Early-stage transportation lock-down and quarantine problem
Yida Ding1, Sebastian Wandelt2, Xiaoqian Sun1,2
1School of General Engineering, Beihang University, 100191 Beijing, China.
Transportation Research. Part C, Emerging Technologies
|October 31, 2022
Summary
We developed the Early-stage Transportation Lock-down and Quarantine Problem (TLQP) to optimize pandemic control. This model identifies key transportation restrictions to minimize social costs during outbreaks like COVID-19.
Area of Science:
- Epidemiology and Public Health
- Operations Research
- Network Science
Background:
- Pandemics like COVID-19 highlight societal vulnerability and the need for effective outbreak containment strategies.
- Restricting population movement is a critical measure, but decisions require informed, optimized approaches.
Purpose of the Study:
- To propose the Early-stage Transportation Lock-down and Quarantine Problem (TLQP) for optimizing transportation infrastructure restrictions during pandemics.
- To develop a decision framework for minimizing the social cost of movement restrictions.
Main Methods:
- Integration of a network-based Susceptible-Exposed-Infectious-Recovered (SEIR) model with a combinatorial optimization framework.
- Development of a heuristic algorithm combining Effective Distance (ED) path and Cost-Effective Lazy Forward (CELF) for intractable problem-solving.
Main Results:
- The TLQP framework provides a method to identify optimal transportation network segments for lockdown to mitigate pandemic spread.
- Experiments on global COVID-19 spread demonstrate the model's utility in guiding national protective measures.
Conclusions:
- The TLQP offers a valuable tool for informed decision-making in pandemic response, balancing public health with socio-economic impact.
- This study contributes to the strategic orchestration of measures for managing current and future epidemic outbreaks effectively.
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